Measuring Radicalization in Online Social Networks Using Markov Chains

REFERENCES

• Anderson, W.J. (1991). Continuous-time Markov chains: An applications-oriented approach. New York, NY: Springer-Verlag.
   Google Scholar
• Apolloni, A. Channakeshava, K. Durbeck, L. Khan, M. Kuhlman, C. Lewis, B. Swarup, S. (2009). A study of information diffusion over a realistic social network model. In Proceedings of the 2009 International Conference on Computational Science and Engineering - CSE 09, August 29–31, Vancouver, Canada, Volume 4 (pp. 675–682). Washington, DC: IEEE Computer Society.
   Google Scholar
• Chau, M., & Xu, J. (2007). Mining communities and their relationships in blogs: A study of online hate groups. International Journal of Human Computer Studies, 65, 57–70.
   Web of Science ®Google Scholar
• Chen, H. (2008, June 17–20). Sentiment and affect analysis of dark web forums: Measuring radicalization on the Internet. ISI 2008, Taipei, Taiwan.
   Google Scholar
• Chen, W., Wang, C., & Wang, Y. (2010, July). Scalable influence maximization for prevalent viral marketing in large-scale social networks. In R. Bharat, K. Balaji, T. Andrew, & Y. Qiang (Eds.), Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM (pp. 1029–1038). . New York, NY: ACM.
   Google Scholar
• Ghosh, R., & Lerman, K. (2010, July). Predicting influential users in online social networks. Paper presented at The Fourth SNA-KDD Workshop, held in conjunction withThe 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC.
   Google Scholar
• Gotz, M., Leskovec, J., McGlohon, M., & Faloutsos, C. (2009, May). Modelling blog dynamics. In International Conference on Weblogs and Social Media, 2009 (pp. 26–33). San Jose, CA: AAAI.
   Google Scholar
• Goyal, A., Bonchi, F., & Lakshmanan, L.V. S. (2010). Learning influence probabilities in social networks. In Proceedings of Third ACM International Conference on Web Search and Data Mining, WSDM, 2010 (pp. 241–250). New York, NY: ACM.
   Google Scholar
• Jingxuan, L., Wei, P., Tao, L., & Tong, S. (2013, April). Network user influence dynamics prediction. In Y. Ishikaw, J. Li, W. Wang, R. Zhang, W. Zhang (Eds.), Proceedings of the 15th Asia-Pacific Web Conference, APWeb 2013, Sydney, Australia, April 4–6, 2013 (pp. 310–322). Lecture Notes in Computer Science, Volume 7808. New York, NY: Springer.
   Google Scholar
• Kempe, D., Kleinberg, J., & Tardos, E. (2003, August). Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2003 (pp. 137–146). New York, NY: ACM.
   Google Scholar
• Kempe, D., Kleinberg, J., & Tardos, E. (2005). Influential nodes in a diffusion model for social networks. In L. Caires, G. F. Italiano, L. Monteiro, C. Palamidessi, & M. Yung (Eds.), Proceedings of the 32nd International Conference on Automata, Languages and Programming ICALP'05 (pp. 1127–1138). Berlin, Germany: Springer-Verlag.
   Google Scholar
• Levin, P., Lefebvre, J., & Perkins, T.J. (2012). What do molecules do when we are not looking? State sequence analysis for stochastic chemical systems. Journal of the Royal Society, 9(77), 3411–3425. doi:10.1098/rsif.2012.0633
   Web of Science ®Google Scholar
• Levin, P., & Perkins, T. (2012). Computing most probable sequences of state transitions in continuous-time Markov systems (Thesis submitted in partial fulfillment of the requirements of the degree of Master of Science). University of Ottawa, Ottawa, Canada.
   Google Scholar
• L’Huiller, G., Alvarez, H.A., Rios, S., & Aguilera, F. (2010, July). Topic-based social network analysis for virtual communities of interests in the dark web. Paper presented at ISI-KDD2010, 2010, Washington, DC.
   Google Scholar
• Moss, S., & Edmonds, B. (2005). Towards good social science. Journal of Artificial Societies and Social Simulation, 8(4), 1–13.
   Web of Science ®Google Scholar
• Puterman, M.L. (1994). Markov decision processes: Discrete stochastic dynamic programming. New York, NY: John Wiley and Sons.
   Google Scholar
• Saito, K., Kimura, M., Ohara, K., & Motoda, H. (2010), Efficient estimation of cumulative influence for multiple activation information diffusion model with continuous time delay. In B.-T. Zhang & M.A. Orgun (Eds.),PRICAI 2010. LNCS, vol. 6230 (pp. 244–255). Heidelberg, Germany: Springer.
   Google Scholar
• Stevens, T., & Neumann, P.R. (2009). Countering online radicalisation, a strategy for action. A policy report published by the International Centre for the Study of Radicalization and Political Violence (ICSR). Retrieved from http://icsr.info/wp-content/uploads/2012/10/1236768491ICSROnlineRadicalisationReport.pdf
   Google Scholar
• Tayebi, M.A. (2011, December). Organized crime structures in co-offending networks. Paper presented at Ninth International Conference on Dependable, Autonomic and Secure Computing (DASC), 2011 IEEE. Sydney, New South Wales, Australia.
   Google Scholar
• Tsur, O., & Rappoport, A. (2012). What's in a hashtag? Content based prediction of the spread of ideas in microblogging communities. In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining (WSDM), February 8–12, 2012, Seattle, Washington, USA (pp. 643–652). New York, NY: ACM.
   Google Scholar
• Wadhwa, P., & Bhatia, M.P. S. (2013, August). Analyzing radicalization on Twitter. In SRS'2013 (Student Research Symposium) collocated with International Conference on Advances in Computing, Communications and Informatics (ICACCI). Chennai, India: Research Publishing Services. doi:10.3850/978-981-07-7393-9_041
   Google Scholar
• Wadhwa, P., & Bhatia, M.P. S. (2014). Classification of radical messages in Twitter using security associations. In Biju, I. (Eds.), Case studies in secure computing: Achievements and trends (pp. 273–294). Boca Raton, FL: CRC Press.
   Google Scholar
• Weng, J., Lim, E.P., Jiang, J., & He, Q. (2010). Twitterrank: Finding topic sensitive influential twitterers. Proceedings of WSDM’10 (pp. 261–270). New York, NY: ACM.
   Google Scholar
• Wilson, C., Sala, A., Puttaswamy, K.P. N., & Zhao, B.Y. (2012, November). Beyond social graphs: User interactions in online social network and their implications. ACM Transactions on Web, 6(4), Article 17(1–30).
   Web of Science ®Google Scholar